Search algorithms provide methods for ranking a web scale collection of documents given a short query. The success of these algorithms often relies on the rich set of document properties or features and the complex relationships between them. Increasingly, machine learning techniques are being used to learn these relationships for an effective ranking function.
One of the fundamental problems for a web search engine is the development of ranking models for different markets. While the approach of training a single model for all markets is attractive, it may fail to fully exploit specific properties of the markets. On the other hand, the approach of training market specific models often requires a huge overhead related to acquiring a large training set for each market.